Stores the output of Bayesian variable selection, as produced by
The class extends a list, so all usual methods for lists also work for
msfit objects, e.g. accessing elements, retrieving names etc.
Some additional methods are provided for printing information on screen, computing posterior probabilities or sampling from the posterior of regression coefficients, as indicated below.
Typically objects are automatically created by a call to
Alternatively, objects can be created by calls of the form
x is a list with the adequate
elements (see slots).
The class extends a list with elements:
matrix with posterior samples for the model
indicates that variable j was included in the model in the MCMC
returns posterior samples for parameters other than the model
indicator, i.e. basically hyper-parameters.
If hyper-parameters were fixed in the model specification,
postOther will be empty.
Marginal posterior probability for inclusion of each
covariate. This is computed by averaging marginal post prob for
inclusion in each Gibbs iteration, which is much more accurate than
Model with highest posterior probability amongst all those visited
Unnormalized posterior prob of posterior mode (log scale)
Unnormalized posterior prob of each visited model (log scale)
Estimated coefficients (via posterior mode) for highest posterior probability model
Residual distribution, i.e. argument
Number of variables
signature(object = "msfit"): Displays general information about the object.
signature(object = "msfit"): Extracts
posterior model probabilities.
signature(object = "msfit"): Obtain posterior
samples for regression coefficients.
Johnson VE, Rossell D. Non-Local Prior Densities for Default Bayesian Hypothesis Tests. Journal of the Royal Statistical Society B, 2010, 72, 143-170
Johnson VE, Rossell D. Bayesian model selection in high-dimensional settings. Journal of the American Statistical Association, 107, 498:649-660.